The dataset viewer is not available for this dataset.
Error code: ConfigNamesError
Exception: TypeError
Message: Value.__init__() missing 1 required positional argument: 'dtype'
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 67, in compute_config_names_response
config_names = get_dataset_config_names(
path=dataset,
token=hf_token,
)
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 161, in get_dataset_config_names
dataset_module = dataset_module_factory(
path,
...<4 lines>...
**download_kwargs,
)
File "/usr/local/lib/python3.14/site-packages/datasets/load.py", line 1217, in dataset_module_factory
raise e1 from None
File "/usr/local/lib/python3.14/site-packages/datasets/load.py", line 1192, in dataset_module_factory
).get_module()
~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/load.py", line 700, in get_module
config_name: DatasetInfo.from_dict(dataset_info_dict)
~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/info.py", line 284, in from_dict
return cls(**{k: v for k, v in dataset_info_dict.items() if k in field_names})
File "<string>", line 20, in __init__
File "/usr/local/lib/python3.14/site-packages/datasets/info.py", line 170, in __post_init__
self.features = Features.from_dict(self.features)
~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1993, in from_dict
obj = generate_from_dict(dic)
File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1574, in generate_from_dict
return {key: generate_from_dict(value) for key, value in obj.items()}
~~~~~~~~~~~~~~~~~~^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1593, in generate_from_dict
return class_type(**{k: v for k, v in obj.items() if k in field_names})
TypeError: Value.__init__() missing 1 required positional argument: 'dtype'Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
BENO
Dataset Description
The BENO dataset originates from the ICLR 2024 paper BENO: Boundary-Embedded Neural Operators for Elliptic PDEs and is designed for solving elliptic partial differential equations under complex boundary conditions. The data contains random boundary geometries with four, three, two, one, or no corners, all standardized to a 32 x 32 grid resolution.
Paper: BENO: Boundary-Embedded Neural Operators for Elliptic PDEs
Supported Tasks
| Scenario | Description |
|---|---|
| Elliptic PDE solving | Predict the solution field from the boundary conditions and the right-hand side of the equation. |
| Boundary-condition research | Compare solution performance under Dirichlet and Neumann boundary conditions. |
| Geometry generalization evaluation | Evaluate model generalization across different random boundary shapes. |
| Neural operator research | Provide standardized training and evaluation data for operator models such as BENO. |
Dataset Format and Structure
The data is organized by boundary condition:
data/
Dirichlet/
Neumann/
Each boundary-condition category contains the following six configurations: N32_0c, N32_1c, N32_2c, N32_3c, N32_4c, and N32_mix. Each configuration contains 1,000 float64 samples:
| File | shape | Description |
|---|---|---|
BC_<prefix>_all.npy |
[1000, 128, 4] |
Boundary coordinates, boundary values, and boundary features. |
RHS_<prefix>_all.npy |
[1000, 1024, 4] |
Coordinates, source terms, and cell states on a 32×32 grid. |
SOL_<prefix>_all.npy |
[1000, 1024, 1] |
Elliptic PDE solution fields. |
How to Use the Dataset
This dataset has been adapted for the OneScience-Sugon/BENO model. Download the dataset and model:
hf download --dataset OneScience-Sugon/beno --local-dir ./data
Official OneScience Information
| Platform | OneScience Main Repository | Skills Repository |
|---|---|---|
| Gitee | https://gitee.com/onescience-ai/onescience | https://gitee.com/onescience-ai/oneskills |
| GitHub | https://github.com/onescience-ai/OneScience | https://github.com/onescience-ai/oneskills |
Citation and License
- Original BENO paper: BENO: Boundary-embedded Neural Operators for Elliptic PDEs
- This dataset has been organized and converted from the original BENO dataset. Its use must comply with the licensing requirements published by the original project.
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